1. Introduction

1.1 Tutorial's Goal

This tutorial aims to guide you on how to build Convolutional Neural Networks (CNN) for image classification.

1.2 Learning Objectives

By the end of this tutorial, you will be able to:
1. Understand the structure of CNNs
2. Comprehend the advantages of CNNs
3. Build a CNN model for image classification

1.3 Prerequisites

You will need a basic understanding of Python and Machine Learning. Familiarity with the Keras library would also be beneficial.

2. Step-by-Step Guide

A Convolutional Neural Network (CNN) is a deep learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre-processing required in a CNN is much lower as compared to other classification algorithms.

The architecture of a CNN is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex.

2.1 CNN Structure

A typical CNN consists of three types of layers:
1. Convolutional Layer: This layer uses a set of learnable filters to apply convolution operation on the input.
2. Pooling Layer: It reduces the spatial size of the Convolved Feature to control overfitting.
3. Fully Connected Layer: It computes the class scores which are the final output.

2.2 Advantages of CNN

1. Less Preprocessing: CNNs learn the filters that in traditional algorithms were hand-engineered.
2. Network Architecture: CNNs capture the spatial and temporal dependencies in an image through the application of relevant filters.

3. Code Examples

For our examples, we will use the Keras library in Python.

3.1 Building a Basic CNN Model

# Importing required libraries
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# Initializing the CNN
model = Sequential()

# Step 1 - Convolution
model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
model.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
model.add(Flatten())

# Step 4 - Full connection
model.add(Dense(units = 128, activation = 'relu'))
model.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling the CNN
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

This code builds a simple CNN model with one convolutional layer, one pooling layer, and two fully connected layers.

4. Summary

In this tutorial, you learned about the structure of CNNs, their advantages, and how to build a CNN model for image classification in Python using Keras.

5. Practice Exercises

5.1 Exercise 1

Build a CNN model with two convolutional layers, each followed by a pooling layer, and two fully connected layers.

5.2 Exercise 2

Train the CNN model you built on a dataset of your choice. Compare the performance of your CNN model with a traditional machine learning model.

5.3 Exercise 3

Experiment with different types of activation functions in the convolutional and fully connected layers. Observe and analyze how the choice of activation function impacts the performance of your CNN model.

For further practice, you can experiment with different architectures of CNN and try them on various datasets. Keep learning and practicing!

6. Additional Resources

1. Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press. http://www.deeplearningbook.org/
2. Chollet, F. (2017). Deep Learning with Python. Manning Publications.
3. Stanford University's course on Convolutional Neural Networks for Visual Recognition: http://cs231n.stanford.edu/